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import streamlit as st
from src.data import StoreDataLoader
from src.model import Model_Load
import matplotlib.pyplot as plt
import seaborn as sns
import plotly.graph_objects as go
from sklearn.metrics import mean_absolute_error,mean_squared_error
import numpy as np
import pandas as pd
from src.prediction import test_prediction,val_prediction,create_week_date_featues
import plotly.express as px
#----------------hide menubar and footer----------------------------------
hide_streamlit_style = """
<style>
#MainMenu {visibility: hidden;}
footer {visibility: hidden;}
</style>
"""
st.markdown(hide_streamlit_style, unsafe_allow_html=True)
#-------------------------------------------------------------
## Load model object
model_obj=Model_Load()
#--------------------------------------------------------------
@st.cache_data
def convert_df(df):
return df.to_csv(index=False).encode('utf-8')
#-----------------------------------------------------------------
## Title of Page
st.markdown("""
<div style='text-align: center; margin-top:-70px; margin-bottom: -50px;margin-left: -50px;'>
<h2 style='font-size: 20px; font-family: Courier New, monospace;
letter-spacing: 2px; text-decoration: none;'>
<img src="https://acis.affineanalytics.co.in/assets/images/logo_small.png" alt="logo" width="70" height="30">
<span style='background: linear-gradient(45deg, #ed4965, #c05aaf);
-webkit-background-clip: text;
-webkit-text-fill-color: transparent;
text-shadow: none;'>
Product Demand Forecasting Dashboard
</span>
<span style='font-size: 40%;'>
<sup style='position: relative; top: 5px; color: #ed4965;'>by Affine</sup>
</span>
</h2>
</div>
""", unsafe_allow_html=True)
#---------------------------------------------------------------------------------------------------------------------
# select the model(Sidebar)
with st.sidebar:
st.markdown("""<div style='text-align: left; margin-top:-200px;margin-left:-40px;'>
<img src="https://affine.ai/wp-content/uploads/2023/05/Affine-Logo.svg" alt="logo" width="300" height="60">
</div>""", unsafe_allow_html=True)
option=st.selectbox("Select Model",['TFT','Prophet'])
#------------------------------------------------------------------------------------------------------------
# TFT
if option=='TFT':
#--------------------------------------------------------------------------------------------------------
## TFT data path and load
path='data/train.csv'
obj=StoreDataLoader(path)
train_dataset,test_dataset,training,validation,earliest_time=obj.tft_data()
print(f"TRAINING ::START DATE ::{train_dataset['date'].min()} :: END DATE ::{train_dataset['date'].max()}")
print(f"TESTING ::START DATE ::{test_dataset['date'].min()} :: END DATE ::{test_dataset['date'].max()}")
list_store=train_dataset['store'].unique()
list_items=train_dataset['item'].unique()
#---------------------------------------------------------------------------------------------------------
try:
# load the pre trained tft model
model=model_obj.store_model_load(option)
with st.sidebar:
# st.success('Model Loaded successfully', icon="✅")
# select the store id
store=st.selectbox("Select Store ID",list_store)
# select the item id
item=st.selectbox("Select Product ID",list_items)
#--------------------------------------------------------------------------------------------------------------
## prediction on testing data
testing_results=test_prediction(model,train_dataset=train_dataset,test_dataset=test_dataset
,earliest_time=earliest_time,store_id=store,item_id=item)
# find kpi
rmse=np.around(np.sqrt(mean_squared_error(testing_results['Lead_1'],testing_results['prediction'])),2)
mae=np.around(mean_absolute_error(testing_results['Lead_1'],testing_results['prediction']),2)
print(f"TEST DATA = Item ID : {item} :: MAE : {mae} :: RMSE : {rmse}")
#--------------------------------------tft future prediction-------------------------------------------
final_data=pd.concat([train_dataset,test_dataset])
consumer_data=final_data.loc[(final_data['store']==store) & (final_data['item']==item)]
consumer_data.fillna(0,inplace=True)
date_list=[]
demand_prediction=[]
for i in range(30):
# select last 150 records as an enocer + decoder data
encoder_data = consumer_data[lambda x: x.days_from_start > x.days_from_start.max() - 150]
last_data = consumer_data[lambda x: x.days_from_start == x.days_from_start.max()]
# prediction date and time
date_list.append(encoder_data.tail(1).iloc[-1,:]['date'])
# prediction for the last 30 records
test_prediction = model.predict(encoder_data,
mode="prediction",
trainer_kwargs=dict(accelerator="cpu"),
return_x=True)
# create the next day record
decoder_data = pd.concat(
[last_data.assign(date=lambda x: x.date + pd.offsets.DateOffset(i)) for i in range(1, 2)],
ignore_index=True,
)
# find the hours_from_start & days_from_start
decoder_data["hours_from_start"] = (decoder_data["date"] - earliest_time).dt.seconds / 60 / 60 + (decoder_data["date"] - earliest_time).dt.days * 24
decoder_data['hours_from_start'] = decoder_data['hours_from_start'].astype('int')
decoder_data["hours_from_start"] += encoder_data["hours_from_start"].max() + 1 - decoder_data["hours_from_start"].min()
# add time index consistent with "data"
decoder_data["days_from_start"] = (decoder_data["date"] - earliest_time).apply(lambda x:x.days)
# adding the datetime features
decoder_data=create_week_date_featues(decoder_data,'date')
# last timestep predicted record as assume next day actual demand(for more day forecasting)
decoder_data['sales']=float(test_prediction.output[0][-1])
# append this prediction into the list
demand_prediction.append(float(test_prediction.output[0][-1]))
# update prediction time idx
decoder_data['time_idx']=int(test_prediction.x['decoder_time_idx'][0][-1])
# add the next day record into the original data
consumer_data=pd.concat([consumer_data,decoder_data])
# fina lag and update
consumer_data['lag_1']=consumer_data['sales'].shift(1)
consumer_data['lag_5']=consumer_data['sales'].shift(5)
# reset the index
consumer_data=consumer_data.reset_index(drop=True)
# forecast values for the next 30 days/timesteps
d2=pd.DataFrame({"date":date_list,"prediction":demand_prediction})[['date','prediction']]
# update the store and item ids
d2['store']=store
d2['item']=item
#----------------------------TFT and Prophet model KPI----------------------------------------
with st.sidebar:
st.markdown(f"""
<style>
/* Sidebar header style */
.sidebar-header {{
padding: 1px;
background-color: #9966FF;
text-align: center;
font-size: 13px;
font-weight: bold;
color: #FFF ;
}}
</style>
<div class="sidebar-header">
Models Evalution
</div>
""",unsafe_allow_html=True)
st.dataframe(pd.DataFrame({"KPI":['RMSE','MAE'],"TFT":[7.73,6.17],"Prophet":[7.32,6.01]}).set_index('KPI'),width=300)
# d2=pd.DataFrame({"KPI":['RMSE','MAE','RMSE','MAE'],"model":['TFT','TFT','Prophet','Prophet'],"Score":[7.73,6.17,7.32,6.01]})
# fig = px.bar(d2, x="KPI", y="Score",
# color='model', barmode='group',
# height=200,width=300,text_auto=True,)
# st.plotly_chart(fig)
#------------------------------------Prophet model KPI---------------------------------------------------------
st.markdown(f"""
<style>
/* Sidebar header style */
.sidebar-header {{
padding: 3px;
background-color:linear-gradient(45deg, #ed4965, #c05aaf);
text-align: center;
font-size: 13px;
font-weight: bold;
color: #FFF ;
}}
</style>
<div class="sidebar-header">
KPI :: {item}
</div>
""",unsafe_allow_html=True)
st.dataframe(pd.DataFrame({"KPI":['RMSE','MAE'],"TFT":[rmse,mae]}).set_index('KPI'),width=300)
#--------------------------------------------------------------------------------------------------------------
# tabs
tab1,tab2=st.tabs(['📈Forecast Plot','🗃Forecast Table']) #tab3-'🗃Actual Table'
#------------------------------------------------Tab-1-----------------------------------------------------------
tab1.markdown("""
<div style='text-align: left; margin-top:-10px;margin-bottom:-10px;'>
<h2 style='font-size: 30px; font-family: Palatino, serif;
letter-spacing: 2px; text-decoration: none;'>
📈
<span style='background: linear-gradient(45deg, #ed4965, #c05aaf);
-webkit-background-clip: text;
-webkit-text-fill-color: transparent;
text-shadow: none;'>
Forecast Plot
</span>
<span style='font-size: 40%;'>
<sup style='position: relative; top: 5px; color: #ed4965;'></sup>
</span>
</h2>
</div>
""", unsafe_allow_html=True)
# change dtype on prediction column
testing_results['prediction']=testing_results['prediction'].apply(lambda x:round(x))
testing_results['date']=testing_results['date'].dt.date
d2['prediction']=d2['prediction'].apply(lambda x:round(x))
d2['date']=d2['date'].dt.date
# training_data=train_dataset.loc[(train_dataset['store']==store)&(train_dataset['item']==item)][['date','Lead_1']].iloc[-60:,:]
#---------------------------------------------forecast plot---------------------------------------------
fig = go.Figure([
# go.Scatter(x=training_data['date'],y=training_data['Lead_1'],name='Train Observed',line=dict(color='rgba(50, 205, 50, 0.7)')),
#go.Scatter(x=y_train_pred['ds'],y=y_train_pred['yhat'],name='Prophet Pred.(10 Item)',line=dict(color='blue', dash='dot')),
go.Scatter(x=testing_results['date'], y=testing_results['Lead_1'],name='Observed',line=dict(color='rgba(218, 112, 214, 0.5)')),
go.Scatter(x=testing_results['date'],y=testing_results['prediction'],name='Historical Forecast',line=dict(color='#9400D3', dash='dash')),
go.Scatter(x=d2['date'],y=d2['prediction'],name='Future Forecast',line=dict(color='Dark Orange', dash='dot'))])
fig.update_layout(
xaxis_title='Date',
yaxis_title='Order Demand',
margin=dict(l=0, r=0, t=50, b=0),
xaxis=dict(title_font=dict(size=20)),
yaxis=dict(title_font=dict(size=20)))
fig.update_layout(width=700,height=400)
tab1.plotly_chart(fig)
#----------------------------------------------Tab-2------------------------------------------------------------
tab2.markdown("""
<div style='text-align: left; margin-top:-10px;'>
<h2 style='font-size: 30px; font-family: Palatino, serif;
letter-spacing: 2px; text-decoration: none;'>
📃
<span style='background: linear-gradient(45deg, #ed4965, #c05aaf);
-webkit-background-clip: text;
-webkit-text-fill-color: transparent;
text-shadow: none;'>
Forecast Table
</span>
<span style='font-size: 40%;'>
<sup style='position: relative; top: 5px; color: #ed4965;'></sup>
</span>
</h2>
</div>
""", unsafe_allow_html=True)
final_r=pd.concat([d2[['date','store','item','prediction']],testing_results[['date','store','item','prediction']]]).sort_values('date').drop_duplicates().reset_index(drop=True)
csv = convert_df(final_r)
tab2.dataframe(final_r,width=500)
tab2.download_button(
"Download",
csv,
"file.csv",
"text/csv",
key='download-csv'
)
#--------------------------------Tab-3----------------------------------------------
# tab3.markdown("""
# <div style='text-align: left; margin-top:-10px;margin-bottom:-10px;'>
# <h2 style='font-size: 30px; font-family: Palatino, serif;
# letter-spacing: 2px; text-decoration: none;'>
# 📈
# <span style='background: linear-gradient(45deg, #ed4965, #c05aaf);
# -webkit-background-clip: text;
# -webkit-text-fill-color: transparent;
# text-shadow: none;'>
# Actual Dataset
# </span>
# <span style='font-size: 40%;'>
# <sup style='position: relative; top: 5px; color: #ed4965;'></sup>
# </span>
# </h2>
# </div>
# """, unsafe_allow_html=True)
# train_a=train_dataset.loc[(train_dataset['store']==store) & (train_dataset['item']==item)][['date','store','item','sales']]
# test_a=test_dataset.loc[(test_dataset['store']==store) & (test_dataset['item']==item)][['date','store','item','sales']]
# actual_final_data=pd.concat([train_a,test_a])
# actual_final_data['date']=actual_final_data['date'].dt.date
# tab3.dataframe(actual_final_data,width=500)
except:
st.sidebar.error('Model Not Loaded successfully!',icon="🚨")
#+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
#+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
elif option=='Prophet':
print("prophet")
#---------------------------------------------------Data----------------------------------------------------
# Prophet data
path='data/train.csv'
obj=StoreDataLoader(path)
fb_train_data,fb_test_data,item_dummay,store_dummay=obj.fb_data()
# st.write(fb_train_data.columns)
# st.write(fb_test_data.columns)
# print(fb_test_data.columns)
print(f"TRAINING ::START DATE ::{fb_train_data['ds'].min()} :: END DATE ::{fb_train_data['ds'].max()}")
print(f"TESTING ::START DATE ::{fb_test_data['ds'].min()} :: END DATE ::{fb_test_data['ds'].max()}")
train_new=fb_train_data.drop('y',axis=1)
test_new=fb_test_data.drop('y',axis=1)
#----------------------------------------------model Load----------------------------------------------------
try:
fb_model=model_obj.store_model_load(option)
# with st.sidebar:
# st.success('Model Loaded successfully', icon="✅")
#-------------------------------------select store & item ---------------------------------------------------
list_items=item_dummay.columns
list_store=store_dummay.columns
with st.sidebar:
store=st.selectbox("Select Store",list_store)
item=st.selectbox("Select Product",list_items)
#------------------------------------------prediction---------------------------------------------------------------
test_prediction=fb_model.predict(test_new.loc[test_new[item]==1])
train_prediction=fb_model.predict(train_new.loc[train_new[item]==1])
y_true_test=fb_test_data.loc[fb_test_data[item]==1]
y_true_train=fb_train_data.loc[fb_train_data[item]==1]
y_train_pred=train_prediction[['ds','yhat']].iloc[-60:,:]
y_train_true=y_true_train[['ds','y']].iloc[-60:,:]
y_test_pred=test_prediction[['ds','yhat']]
y_test_true=y_true_test[['ds','y']]
#----------------------------------------KPI---------------------------------------------------------------
rmse=np.sqrt(mean_squared_error(y_test_true['y'],y_test_pred['yhat']))
mae=mean_absolute_error(y_test_true['y'],y_test_pred['yhat'])
#---------------------------------future prediction---------------------------------------
fb_final=pd.concat([fb_train_data,fb_test_data])
# extract the data for selected store and item
fb_consumer=fb_final.loc[(fb_final[store]==1) & (fb_final[item]==1)]
# list of dates and prediction
date_list=[]
prediction_list=[]
# predicting the next 30 days product demand
for i in range(30):
# select only date record
next_prediction=fb_consumer.tail(1).drop('y',axis=1) # drop target of last 01/01/2015 00:00:00
# predict next timestep demand
prediction=fb_model.predict(next_prediction) # pass other feature value to the model
# append date and predicted demand
date_list.append(prediction['ds'][0]) ## append the datetime of prediction
prediction_list.append(prediction['yhat'][0]) ## append the next timestep prediction
#--------------------------next timestep data simulate-------------------------------------------------------------
last_data = fb_consumer[lambda x: x.ds == x.ds.max()] # last date present in data
# next timestep
decoder_data = pd.concat(
[last_data.assign(ds=lambda x: x.ds + pd.offsets.DateOffset(i)) for i in range(1, 2)],
ignore_index=True,
)
# update next timestep datetime covariates
decoder_data=create_week_date_featues(decoder_data,'ds')
# update last day demand prediction to the here as an actual demand value(using for more future timestep prediction)
decoder_data['sales']=prediction['yhat'][0] # assume next timestep prediction as actual
# update this next record into the original data
fb_consumer=pd.concat([fb_consumer,decoder_data]) # append that next timestep data to into main data
# find shift of power usage and update into the datset
fb_consumer['lag_1']=fb_consumer['sales'].shift(1)
fb_consumer['lag_5']=fb_consumer['sales'].shift(5)
fb_consumer=fb_consumer.reset_index(drop=True) # reset_index
future_prediction=pd.DataFrame({"ds":date_list,"yhat":prediction_list})
future_prediction['store']=store
future_prediction['item']=item
with st.sidebar:
st.markdown(f"""
<style>
/* Sidebar header style */
.sidebar-header {{
padding: 1px;
background-color: #9966FF;
text-align: center;
font-size: 13px;
font-weight: bold;
color: #FFF ;
}}
</style>
<div class="sidebar-header">
Models Evalution
</div>
""",unsafe_allow_html=True)
st.dataframe(pd.DataFrame({"KPI":['RMSE','MAE'],"TFT":[7.73,6.17],"Prophet":[7.32,6.01]}).set_index('KPI'),width=300)
st.markdown(f"""
<style>
/* Sidebar header style */
.sidebar-header {{
padding: 3px;
background-color:linear-gradient(45deg, #ed4965, #c05aaf);
text-align: center;
font-size: 13px;
font-weight: bold;
color: #FFF ;
}}
</style>
<div class="sidebar-header">
KPI :: {item}
</div>
""",unsafe_allow_html=True)
st.dataframe(pd.DataFrame({"KPI":['RMSE','MAE'],"Prophet":[rmse,mae]}).set_index('KPI'),width=300)
#---------------------------------------Tabs-----------------------------------------------------------------------
tab1,tab2=st.tabs(['📈Forecast Plot','🗃Forecast Table']) #tab3- '🗃Actual Table'
#-------------------------------------------Tab-1=Forecast plot---------------------------------------------------
tab1.markdown("""
<div style='text-align: left; margin-top:-10px;margin-bottom:-10px;'>
<h2 style='font-size: 30px; font-family: Palatino, serif;
letter-spacing: 2px; text-decoration: none;'>
📈
<span style='background: linear-gradient(45deg, #ed4965, #c05aaf);
-webkit-background-clip: text;
-webkit-text-fill-color: transparent;
text-shadow: none;'>
Forecast Plot
</span>
<span style='font-size: 40%;'>
<sup style='position: relative; top: 5px; color: #ed4965;'></sup>
</span>
</h2>
</div>
""", unsafe_allow_html=True)
## round fig.
y_train_true['y']=y_train_true['y'].astype('int')
y_train_pred['yhat']=y_train_pred['yhat'].astype('int')
y_test_true['y']=y_test_true['y'].astype('int')
y_test_pred['yhat']=y_test_pred['yhat'].astype('int')
future_prediction['yhat']=future_prediction['yhat'].astype('int')
y_train_true['ds']=y_train_true['ds'].dt.date
y_train_pred['ds']=y_train_pred['ds'].dt.date
y_test_true['ds']=y_test_true['ds'].dt.date
y_test_pred['ds']=y_test_pred['ds'].dt.date
future_prediction['ds']=future_prediction['ds'].dt.date
#-----------------------------plot---------------------------------------------------------------------------------------------
fig = go.Figure([
# go.Scatter(x=y_train_true['ds'],y=y_train_true['y'],name='Train Observed',line=dict(color='rgba(50, 205, 50, 0.7)' )),
# go.Scatter(x=y_train_pred['ds'],y=y_train_pred['yhat'],name='Prophet Pred.(10 Item)',line=dict(color='#32CD32', dash='dot')),
go.Scatter(x=y_test_true['ds'], y=y_test_true['y'],name='Observed',line=dict(color='rgba(218, 112, 214, 0.5)')),
go.Scatter(x=y_test_pred['ds'],y=y_test_pred['yhat'],name='Historical Forecast',line=dict(color='#9400D3', dash='dash')),
go.Scatter(x=future_prediction['ds'],y=future_prediction['yhat'],name='Future Forecast',line=dict(color='Dark Orange', dash='dot'))])
fig.update_layout(
xaxis_title='Date',
yaxis_title='Order Demand',
margin=dict(l=0, r=0, t=50, b=0),
xaxis=dict(title_font=dict(size=20)),
yaxis=dict(title_font=dict(size=20)))
fig.update_layout(width=700,height=400)
tab1.plotly_chart(fig)
#----------------------------------------Tab-2------------------------------------------------------------
results=y_test_pred.reset_index()
results['store']='store_1'
results['item']=item
tab2.markdown("""
<div style='text-align: left; margin-top:-10px;'>
<h2 style='font-size: 30px; font-family: Palatino, serif;
letter-spacing: 2px; text-decoration: none;'>
📃
<span style='background: linear-gradient(45deg, #ed4965, #c05aaf);
-webkit-background-clip: text;
-webkit-text-fill-color: transparent;
text-shadow: none;'>
Forecast Table
</span>
<span style='font-size: 40%;'>
<sup style='position: relative; top: 5px; color: #ed4965;'></sup>
</span>
</h2>
</div>
""", unsafe_allow_html=True)
final_r=pd.concat([future_prediction[['ds','store','item','yhat']],results[['ds','store','item','yhat']]]).sort_values('ds').drop_duplicates().reset_index(drop=True)
csv = convert_df(final_r)
tab2.dataframe(final_r,width=500)
tab2.download_button(
"Download",
csv,
"file.csv",
"text/csv",
key='download-csv'
)
#------------------------------------------Tab-3--------------------------------------------------
# train_a=fb_train_data.loc[fb_train_data[item]==1][['ds','sales']]
# # train_a['store']=1
# # train_a['item']=item
# test_a=fb_test_data.loc[fb_test_data[item]==1][['ds','sales']]
# # test_a['store']=1
# # test_a['item']=item.split('_')[-1]
# actual_final_data=pd.concat([train_a,test_a])
# actual_final_data['store']=1
# actual_final_data['item']=item.split('_')[-1]
# actual_final_data['ds']=actual_final_data['ds'].dt.date
# actual_final_data.rename({"ds":'date'},inplace=True)
# tab3.dataframe(actual_final_data[['date','store','item','sales']],width=500)
except:
st.sidebar.error('Model Not Loaded successfully!',icon="🚨")
|